早期检测心力衰竭使用住院病人纵向电子健康记录
Ignat Drozdov1, Benjamin Szubert1, Clare Murphy2
1Bering Limited, London, United Kingdom.
PloS one
|December 18, 2024
概括
变压器神经网络可以使用电子健康记录预测首次心力衰竭住院 (HHF). 这种人工智能模型为一般人群早期发现心力衰竭风险提供了一种新的方法.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 心力衰竭 (HF) 是一种普遍的疾病,具有重大经济影响.
- 目前在一般人群中对HF的早期检测方法有限.
- 需要可访问的工具来识别高风险住院的人.
研究的目的:
- 评估变压器神经网络,以预测由于心力衰竭 (HHF) 首次住院的风险.
- 用例行收集的二级保健电子健康记录 (EHR) 来评估模型的性能.
- 为了确定模型在三年窗口的未来预测能力.
主要方法:
- 一个变压器神经网络在从二级护理中获得的五年纵向EHR数据上受过训练.
- 该数据集包括人口统计数据,急救和急救出席,住院,药物,实验室结果和生命体征.
- 该模型在一个独立的队列中得到验证,评估了AUROC,灵敏度,特异性和PPV等指标.
主要成果:
- 该模型在测试组中实现了0.86的ROC曲线下的面积 (AUROC).
- 观察到高特异性 (98.26%) 和正预测值 (PPV) 的69.88%.
- 在90%的概率值下,该模型实现了100%的PPV,这表明对积极预测的信心很高.
结论:
- 常规收集的二级护理EHR数据可以有效地用于识别患有第一个HHF风险增加的患者.
- 变压器模型在各种患者群体中表现出强的表现,无论性别或社会经济地位如何.
- 这种人工智能驱动的方法为一般人群中早期高频风险分层提供了一个有希望的策略.
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